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Record W4290959419 · doi:10.6004/jnccn.2022.7029

Regional Variations in Clinical Trial Outcomes in Oncology

2022· review· en· W4290959419 on OpenAlexaff
Brooke E. Wilson, Sallie‐Anne Pearson, Michael Bartoň, Eitan Amir

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2022
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineHazard ratioSubgroup analysisRandom effects modelInternal medicineHigh income countriesClinical endpointCohortClinical trialDemographyOncologyConfidence intervalMeta-analysisDeveloping country

Abstract

fetched live from OpenAlex

BACKGROUND: It is unknown how often regional differences in oncology trials are observed. Based on our study findings, we quantified regional variation in registration studies in oncology and developed a question guide to help clinicians evaluate regional differences. METHODS: Using FDA archives, we identified registration studies in solid tumor malignancies from 2010 to 2020. We extracted the baseline study characteristics and participating countries and determined whether the primary publication reported a regional subgroup analysis. For studies presenting outcomes stratified by region, we extracted the stratified hazard ratios (HRs) and extracted or calculated the test for heterogeneity. We performed a random effects meta-analysis and a pairwise comparison to determine whether outcomes differed between high-income versus mixed-income regions. RESULTS: We included 147 studies in our final analysis. Studies supporting FDA drug approval have become increasingly multinational over time (β = 0.5; P=.04). The median proportion of countries from high-income groups was 81.2% (range, 44%-100%), with no participation from low-income countries in our cohort. Regional subgroup analysis was presented for 78 studies (53%). Regional heterogeneity was found in 17.8% (8/45) and 18% (8/44) of studies presenting an overall survival (OS) and progression-free survival endpoint, respectively. After grouping regions by income level, we found no difference in OS outcomes in high-income regions compared with mixed-income regions (n=20; HR, 0.95; 95% CI, 0.84-1.07). To determine whether regional variation is genuine, clinicians should evaluate the data according to the following 5 questions: (1) Are the regional groupings logical? (2) Is the regional difference on an absolute or relative scale? (3) Is the regional difference consistent and plausible? (4) Is the regional difference statistically significant? (5) Is there a clinical explanation? CONCLUSIONS: As registration studies in oncology become increasingly international, regional variations in trial outcomes may be detected. The question guide herein will help clinicians determine whether regional variations are likely to be clinically meaningful or statistical anomalies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.553
GPT teacher head0.557
Teacher spread0.004 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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